Overview

Dataset statistics

Number of variables11
Number of observations6076546
Missing cells170098
Missing cells (%)0.3%
Duplicate rows25
Duplicate rows (%)< 0.1%
Total size in memory556.3 MiB
Average record size in memory96.0 B

Variable types

Numeric10
Categorical1

Alerts

Dataset has 25 (< 0.1%) duplicate rowsDuplicates
HauteurNeige is highly imbalanced (96.1%)Imbalance
HauteurNeige has 164016 (2.7%) missing valuesMissing
longitude_scaled is highly skewed (γ1 = -1120.809232)Skewed
latitude_scaled is highly skewed (γ1 = -123.3389005)Skewed
Precipitations has 5301734 (87.2%) zerosZeros
y has 966127 (15.9%) zerosZeros

Reproduction

Analysis started2025-11-26 12:37:20.982742
Analysis finished2025-11-26 12:40:23.309307
Duration3 minutes and 2.33 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

total_count
Real number (ℝ)

Distinct406
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6860057
Minimum1
Maximum1669
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.7 MiB
2025-11-26T13:40:23.419356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q311
95-th percentile30
Maximum1669
Range1668
Interquartile range (IQR)10

Descriptive statistics

Standard deviation11.635925
Coefficient of variation (CV)1.3396175
Kurtosis170.83221
Mean8.6860057
Median Absolute Deviation (MAD)3
Skewness5.3306861
Sum52780913
Variance135.39475
MonotonicityNot monotonic
2025-11-26T13:40:23.589841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11532442
25.2%
2695099
11.4%
3455142
 
7.5%
4369557
 
6.1%
5309688
 
5.1%
6271329
 
4.5%
7236941
 
3.9%
8211716
 
3.5%
9186786
 
3.1%
10165888
 
2.7%
Other values (396)1641958
27.0%
ValueCountFrequency (%)
11532442
25.2%
2695099
11.4%
3455142
 
7.5%
4369557
 
6.1%
5309688
 
5.1%
6271329
 
4.5%
7236941
 
3.9%
8211716
 
3.5%
9186786
 
3.1%
10165888
 
2.7%
ValueCountFrequency (%)
16691
< 0.1%
11001
< 0.1%
10921
< 0.1%
8481
< 0.1%
8131
< 0.1%
7751
< 0.1%
7052
< 0.1%
6641
< 0.1%
6461
< 0.1%
6371
< 0.1%

longitude_scaled
Real number (ℝ)

Skewed 

Distinct12717
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99885337
Minimum0
Maximum0.99979883
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size92.7 MiB
2025-11-26T13:40:23.750249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.99838996
Q10.99858576
median0.99885803
Q30.99911666
95-th percentile0.99931586
Maximum0.99979883
Range0.99979883
Interquartile range (IQR)0.0005309

Descriptive statistics

Standard deviation0.0006641794
Coefficient of variation (CV)0.00066494184
Kurtosis1683633.4
Mean0.99885337
Median Absolute Deviation (MAD)0.00026227
Skewness-1120.8092
Sum6069578.4
Variance4.4113428 × 10-7
MonotonicityNot monotonic
2025-11-26T13:40:23.920004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.998896662581
 
< 0.1%
0.99889682492
 
< 0.1%
0.998463452440
 
< 0.1%
0.99932772248
 
< 0.1%
0.99881152160
 
< 0.1%
0.998463332092
 
< 0.1%
0.998471562058
 
< 0.1%
0.998911262039
 
< 0.1%
0.998701162007
 
< 0.1%
0.998730662003
 
< 0.1%
Other values (12707)6054426
99.6%
ValueCountFrequency (%)
02
 
< 0.1%
0.9807776396
< 0.1%
0.992112041
 
< 0.1%
0.996586561
 
< 0.1%
0.99783031
 
< 0.1%
0.99808561
 
< 0.1%
0.998121141
 
< 0.1%
0.998122041
 
< 0.1%
0.99812251
 
< 0.1%
0.99812581
 
< 0.1%
ValueCountFrequency (%)
0.999798831
< 0.1%
0.999794841
< 0.1%
0.99979461
< 0.1%
0.99979331
< 0.1%
0.999792462
< 0.1%
0.99979221
< 0.1%
0.999790131
< 0.1%
0.9997861
< 0.1%
0.999785541
< 0.1%
0.99978461
< 0.1%

latitude_scaled
Real number (ℝ)

Skewed 

Distinct17703
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99611423
Minimum0
Maximum0.9998424
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size92.7 MiB
2025-11-26T13:40:24.077561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.9957593
Q10.99597406
median0.9961518
Q30.99636316
95-th percentile0.9966549
Maximum0.9998424
Range0.9998424
Interquartile range (IQR)0.0003891

Descriptive statistics

Standard deviation0.0080660005
Coefficient of variation (CV)0.0080974654
Kurtosis15227.905
Mean0.99611423
Median Absolute Deviation (MAD)0.0001913
Skewness-123.3389
Sum6052933.9
Variance6.5060364 × 10-5
MonotonicityNot monotonic
2025-11-26T13:40:24.249964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.99629131486
 
< 0.1%
0.99611671457
 
< 0.1%
0.99609321441
 
< 0.1%
0.99629121420
 
< 0.1%
0.99626031385
 
< 0.1%
0.996060131380
 
< 0.1%
0.996127671378
 
< 0.1%
0.996291041376
 
< 0.1%
0.996287171363
 
< 0.1%
0.996120631348
 
< 0.1%
Other values (17693)6062512
99.8%
ValueCountFrequency (%)
04
 
< 0.1%
2.0394697 × 10-7392
< 0.1%
0.0121232212
 
< 0.1%
0.98518661
 
< 0.1%
0.99466491
 
< 0.1%
0.99488941
 
< 0.1%
0.995127261
 
< 0.1%
0.9954591
 
< 0.1%
0.995528461
 
< 0.1%
0.995533051
 
< 0.1%
ValueCountFrequency (%)
0.99984241
< 0.1%
0.999248451
< 0.1%
0.998547551
< 0.1%
0.997480631
< 0.1%
0.997479441
< 0.1%
0.997478541
< 0.1%
0.99747792
< 0.1%
0.997477651
< 0.1%
0.99747721
< 0.1%
0.99747441
< 0.1%

Precipitations
Real number (ℝ)

Zeros 

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11320633
Minimum0
Maximum23.6
Zeros5301734
Zeros (%)87.2%
Negative0
Negative (%)0.0%
Memory size92.7 MiB
2025-11-26T13:40:24.388405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.6
Maximum23.6
Range23.6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.57204083
Coefficient of variation (CV)5.0530815
Kurtosis641.92226
Mean0.11320633
Median Absolute Deviation (MAD)0
Skewness18.747545
Sum687903.5
Variance0.32723072
MonotonicityNot monotonic
2025-11-26T13:40:24.527135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
05301734
87.2%
0.2274241
 
4.5%
0.4114100
 
1.9%
0.691353
 
1.5%
0.858441
 
1.0%
1.244881
 
0.7%
136697
 
0.6%
1.431257
 
0.5%
1.823192
 
0.4%
1.618518
 
0.3%
Other values (29)82132
 
1.4%
ValueCountFrequency (%)
05301734
87.2%
0.2274241
 
4.5%
0.4114100
 
1.9%
0.51629
 
< 0.1%
0.691353
 
1.5%
0.858441
 
1.0%
0.93397
 
0.1%
136697
 
0.6%
1.244881
 
0.7%
1.33618
 
0.1%
ValueCountFrequency (%)
23.61310
 
< 0.1%
12.514
 
< 0.1%
9.81
 
< 0.1%
7.52076
 
< 0.1%
6.12302
< 0.1%
5.814
 
< 0.1%
5.54130
0.1%
51372
 
< 0.1%
4.95395
0.1%
4.61488
 
< 0.1%

HauteurNeige
Categorical

Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing164016
Missing (%)2.7%
Memory size92.7 MiB
0.0
5887677 
1.0
 
24853

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters17737590
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.05887677
96.9%
1.024853
 
0.4%
(Missing)164016
 
2.7%

Length

2025-11-26T13:40:24.638443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-26T13:40:24.702869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.05887677
99.6%
1.024853
 
0.4%

Most occurring characters

ValueCountFrequency (%)
011800207
66.5%
.5912530
33.3%
124853
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)17737590
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
011800207
66.5%
.5912530
33.3%
124853
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)17737590
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
011800207
66.5%
.5912530
33.3%
124853
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)17737590
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
011800207
66.5%
.5912530
33.3%
124853
 
0.1%

Temperature
Real number (ℝ)

Distinct356
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.201098
Minimum-3.6
Maximum35.3
Zeros0
Zeros (%)0.0%
Negative102814
Negative (%)1.7%
Memory size92.7 MiB
2025-11-26T13:40:24.794455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-3.6
5-th percentile4
Q110.5
median14.5
Q320.1
95-th percentile27.1
Maximum35.3
Range38.9
Interquartile range (IQR)9.6

Descriptive statistics

Standard deviation6.9353509
Coefficient of variation (CV)0.45624012
Kurtosis-0.2081353
Mean15.201098
Median Absolute Deviation (MAD)4.7
Skewness0.14761243
Sum92370173
Variance48.099092
MonotonicityNot monotonic
2025-11-26T13:40:24.950097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.860945
 
1.0%
10.556689
 
0.9%
11.354808
 
0.9%
11.554241
 
0.9%
12.651145
 
0.8%
1150304
 
0.8%
11.649256
 
0.8%
11.749002
 
0.8%
11.848087
 
0.8%
11.247543
 
0.8%
Other values (346)5554526
91.4%
ValueCountFrequency (%)
-3.62168
 
< 0.1%
-2.82191
 
< 0.1%
-2.41
 
< 0.1%
-2.32183
 
< 0.1%
-2.11825
 
< 0.1%
-1.91903
 
< 0.1%
-1.82223
 
< 0.1%
-1.72149
 
< 0.1%
-1.69803
0.2%
-1.51828
 
< 0.1%
ValueCountFrequency (%)
35.31807
 
< 0.1%
35.13739
0.1%
34.51561
 
< 0.1%
34.22145
< 0.1%
343109
0.1%
33.71315
 
< 0.1%
33.63541
0.1%
33.54896
0.1%
33.41995
< 0.1%
33.23531
0.1%

ForceVent
Real number (ℝ)

Distinct91
Distinct (%)< 0.1%
Missing6082
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean3.356024
Minimum0
Maximum10.5
Zeros6796
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size92.7 MiB
2025-11-26T13:40:25.867298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.3
Q12.2
median3.2
Q34.2
95-th percentile6
Maximum10.5
Range10.5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4861837
Coefficient of variation (CV)0.4428406
Kurtosis1.0465474
Mean3.356024
Median Absolute Deviation (MAD)1
Skewness0.81449841
Sum20372623
Variance2.208742
MonotonicityNot monotonic
2025-11-26T13:40:25.967932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.5202662
 
3.3%
2.9187855
 
3.1%
3.4187748
 
3.1%
3.2174924
 
2.9%
3.6174599
 
2.9%
2.2173671
 
2.9%
2.7167548
 
2.8%
2.8166305
 
2.7%
3.1165259
 
2.7%
2.4158598
 
2.6%
Other values (81)4311295
70.9%
ValueCountFrequency (%)
06796
 
0.1%
0.52239
 
< 0.1%
0.612839
 
0.2%
0.714216
 
0.2%
0.828004
 
0.5%
0.934794
0.6%
146637
0.8%
1.154602
0.9%
1.273832
1.2%
1.376155
1.3%
ValueCountFrequency (%)
10.52137
 
< 0.1%
9.51863
 
< 0.1%
9.45787
0.1%
9.33458
0.1%
9.16084
0.1%
93837
0.1%
8.91681
 
< 0.1%
8.85429
0.1%
8.71459
 
< 0.1%
8.61814
 
< 0.1%

day_of_week
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5311963
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.7 MiB
2025-11-26T13:40:26.042485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7065866
Coefficient of variation (CV)0.48328849
Kurtosis-1.269461
Mean3.5311963
Median Absolute Deviation (MAD)1
Skewness-0.01991001
Sum21457477
Variance2.9124377
MonotonicityNot monotonic
2025-11-26T13:40:26.109715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
61040088
17.1%
51029642
16.9%
41014603
16.7%
21014412
16.7%
31001851
16.5%
1975950
16.1%
ValueCountFrequency (%)
1975950
16.1%
21014412
16.7%
31001851
16.5%
41014603
16.7%
51029642
16.9%
61040088
17.1%
ValueCountFrequency (%)
61040088
17.1%
51029642
16.9%
41014603
16.7%
31001851
16.5%
21014412
16.7%
1975950
16.1%

month_of_year
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5621916
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.7 MiB
2025-11-26T13:40:26.171948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4636485
Coefficient of variation (CV)0.52781886
Kurtosis-1.2131152
Mean6.5621916
Median Absolute Deviation (MAD)3
Skewness-0.021910514
Sum39875459
Variance11.996861
MonotonicityNot monotonic
2025-11-26T13:40:26.247718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12569353
9.4%
10538948
8.9%
8531972
8.8%
7529927
8.7%
4519078
8.5%
3517284
8.5%
9514097
8.5%
1505486
8.3%
6484050
8.0%
2478240
7.9%
Other values (2)888111
14.6%
ValueCountFrequency (%)
1505486
8.3%
2478240
7.9%
3517284
8.5%
4519078
8.5%
5450341
7.4%
6484050
8.0%
7529927
8.7%
8531972
8.8%
9514097
8.5%
10538948
8.9%
ValueCountFrequency (%)
12569353
9.4%
11437770
7.2%
10538948
8.9%
9514097
8.5%
8531972
8.8%
7529927
8.7%
6484050
8.0%
5450341
7.4%
4519078
8.5%
3517284
8.5%

hour
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.25794
Minimum6
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size92.7 MiB
2025-11-26T13:40:26.324256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile7
Q19
median12
Q315
95-th percentile17
Maximum19
Range13
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.17769
Coefficient of variation (CV)0.25923524
Kurtosis-1.1580699
Mean12.25794
Median Absolute Deviation (MAD)3
Skewness0.052604212
Sum74485937
Variance10.097714
MonotonicityNot monotonic
2025-11-26T13:40:26.414487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
9598697
9.9%
14596622
9.8%
8594690
9.8%
10584009
9.6%
11565575
9.3%
13560183
9.2%
15550682
9.1%
16510814
8.4%
12491668
8.1%
17488246
8.0%
Other values (4)535360
8.8%
ValueCountFrequency (%)
6525
 
< 0.1%
7329269
5.4%
8594690
9.8%
9598697
9.9%
10584009
9.6%
11565575
9.3%
12491668
8.1%
13560183
9.2%
14596622
9.8%
15550682
9.1%
ValueCountFrequency (%)
191969
 
< 0.1%
18203597
 
3.4%
17488246
8.0%
16510814
8.4%
15550682
9.1%
14596622
9.8%
13560183
9.2%
12491668
8.1%
11565575
9.3%
10584009
9.6%

y
Real number (ℝ)

Zeros 

Distinct4411
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50239407
Minimum0
Maximum1
Zeros966127
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size92.7 MiB
2025-11-26T13:40:26.513178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.19444445
median0.46153846
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.80555555

Descriptive statistics

Standard deviation0.36835769
Coefficient of variation (CV)0.73320468
Kurtosis-1.3985008
Mean0.50239407
Median Absolute Deviation (MAD)0.32640333
Skewness0.14810964
Sum3052820.7
Variance0.13568739
MonotonicityNot monotonic
2025-11-26T13:40:26.624178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11617569
26.6%
0966127
15.9%
0.5458986
 
7.6%
0.33333334262398
 
4.3%
0.6666667196996
 
3.2%
0.25169952
 
2.8%
0.2115972
 
1.9%
0.4111653
 
1.8%
0.75103116
 
1.7%
0.681599
 
1.3%
Other values (4401)1992178
32.8%
ValueCountFrequency (%)
0966127
15.9%
0.0072463771
 
< 0.1%
0.0079365081
 
< 0.1%
0.0086956521
 
< 0.1%
0.0090090091
 
< 0.1%
0.009523811
 
< 0.1%
0.0096153851
 
< 0.1%
0.0097087381
 
< 0.1%
0.012
 
< 0.1%
0.0104166672
 
< 0.1%
ValueCountFrequency (%)
11617569
26.6%
0.999084231
 
< 0.1%
0.99858161
 
< 0.1%
0.9984521
 
< 0.1%
0.998415231
 
< 0.1%
0.998349851
 
< 0.1%
0.998260861
 
< 0.1%
0.99822061
 
< 0.1%
0.998217461
 
< 0.1%
0.99812031
 
< 0.1%

Interactions

2025-11-26T13:40:02.063776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:38:54.135509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:01.259395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:08.934128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:15.570649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:23.009248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:31.158139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:39.388337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:46.472772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:54.246771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:40:02.836339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:38:54.983130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:01.935350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:09.603849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:16.271877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:23.752467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:31.950788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:40.176836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:47.213526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:54.971788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:40:03.576388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:38:55.670924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:02.603216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:10.327981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:17.151867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:24.463678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:32.712812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:40.926800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:48.061669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:55.661096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:40:04.422692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:38:56.360227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:03.709227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:11.081600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:17.906352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:25.153269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:33.450209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:41.650467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:48.810559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:56.387187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:40:05.240861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:38:57.040347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:04.496683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:11.754642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:18.645216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:25.893292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:34.279786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:42.542523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:49.564188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:57.093159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:40:06.040354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:38:57.806437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:05.304098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:12.672032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:19.440064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:26.701213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:35.107568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:43.656977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:50.420974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:57.968882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:40:06.744138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:38:58.483111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:06.017605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:12.391391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:20.136508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:27.919785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:36.004434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:43.399112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:51.127379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:59.047084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:40:07.469103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:38:59.168592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:06.715907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:13.057023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:20.829441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:28.763055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:36.817174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:44.166778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:51.964309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:59.858251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:40:08.267470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:38:59.878178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:07.400071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:13.982497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:21.507116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:29.488286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:37.622489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:44.865764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:52.750302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:40:00.565085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:40:08.961236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:00.578221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:08.219437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:14.738964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:22.238472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:30.303853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:38.592401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:45.706823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:39:53.544768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-26T13:40:01.287157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-26T13:40:26.714111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ForceVentHauteurNeigePrecipitationsTemperatureday_of_weekhourlatitude_scaledlongitude_scaledmonth_of_yeartotal_county
ForceVent1.0000.0940.115-0.154-0.0010.143-0.0030.0010.0660.002-0.009
HauteurNeige0.0941.0000.0060.2830.0950.0370.0000.0000.2090.0010.003
Precipitations0.1150.0061.000-0.1520.0250.045-0.0000.0010.0520.0010.001
Temperature-0.1540.283-0.1521.0000.0010.0670.004-0.006-0.296-0.0200.026
day_of_week-0.0010.0950.0250.0011.000-0.007-0.0040.0000.0020.0060.003
hour0.1430.0370.0450.067-0.0071.000-0.013-0.0050.020-0.0110.010
latitude_scaled-0.0030.000-0.0000.004-0.004-0.0131.0000.3190.004-0.0600.080
longitude_scaled0.0010.0000.001-0.0060.000-0.0050.3191.0000.004-0.0230.061
month_of_year0.0660.2090.052-0.2960.0020.0200.0040.0041.0000.002-0.089
total_count0.0020.0010.001-0.0200.006-0.011-0.060-0.0230.0021.000-0.295
y-0.0090.0030.0010.0260.0030.0100.0800.061-0.089-0.2951.000

Missing values

2025-11-26T13:40:09.361104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-26T13:40:12.838328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-26T13:40:19.815985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

total_countlongitude_scaledlatitude_scaledPrecipitationsHauteurNeigeTemperatureForceVentday_of_weekmonth_of_yearhoury
010.9984170.9961180.00.014.62.53480.000000
1350.9992220.9960000.00.022.43.153130.228571
230.9983710.9963091.80.011.73.341081.000000
310.9988040.9963430.00.028.62.642160.000000
420.9991260.9964170.00.09.63.239180.500000
510.9991290.9965070.01.01.53.747110.000000
680.9992560.9966200.00.011.55.135120.250000
790.9990090.9964380.40.015.22.6211150.111111
8200.9989970.9958350.00.023.36.654170.400000
910.9988620.9967010.00.08.94.5310100.000000
total_countlongitude_scaledlatitude_scaledPrecipitationsHauteurNeigeTemperatureForceVentday_of_weekmonth_of_yearhoury
6076536100.9989130.9965040.00.018.63.2410130.500000
6076537120.9990480.9962130.00.010.51.056120.416667
607653890.9988440.9962670.00.018.53.448150.333333
6076539330.9983340.9960070.00.012.25.04880.242424
6076540130.9983250.9962230.00.08.52.846150.846154
607654190.9983970.9961000.00.0-0.32.357180.444444
6076542550.9986550.9960930.00.017.06.0612100.090909
607654390.9992000.9960010.00.011.55.135120.333333
6076544140.9983740.9960760.00.013.63.541170.357143
607654510.9992490.9966040.00.018.02.5410111.000000

Duplicate rows

Most frequently occurring

total_countlongitude_scaledlatitude_scaledPrecipitationsHauteurNeigeTemperatureForceVentday_of_weekmonth_of_yearhoury# duplicates
010.9983660.9962210.00.07.80.836131.02
110.9983950.9962610.00.022.61.94280.02
210.9984630.9960210.00.01.42.267121.02
310.9984870.9962100.01.01.53.747111.02
410.9985340.9958000.00.019.54.1412110.02
510.9986820.9961030.00.020.82.431110.02
610.9987160.9961755.50.017.31.621271.02
710.9987380.9962960.00.06.03.13591.02
810.9987530.9958690.20.013.01.7611120.02
910.9987850.9962770.00.019.22.6611100.02